metadata

annotations_creators:
  - expert-annotated
language:
  - en
license: other
multilinguality: monolingual
dataset_name: Gilt Posture Dataset
task_categories:
  - object-detection
  - image-classification
task_ids:
  - multi-class-image-classification
tags:
  - animal-behavior
  - pigs
  - rgb-d
  - depth-sensing
  - yolo
  - posture

Gilt Posture Recognition Dataset

Each RGB image has a matching depth image (same filename, .png extension).
YOLO-format label files correspond to each image.

🐷 Annotated Postures

Five postures are labeled using YOLO bounding boxes:

Class Name	Class ID
feeding	0
lateral_lying	1
sitting	2
standing	3
sternal_lying	4

📊 Class Distribution

Below is a histogram showing the distribution of posture classes across the dataset:

Dataset Description

The total dataset is split randomly into training, validation, and testing sets (0.75:0.15:0.1). The filename of each image and corresponding labels are assigned with date and time of image captured prefixed by pen and camera identity (p1c1_20250108_080409.png == image of pen1 camera1 captured on January 08, 2025 at 08:04:09 o'clock)

The Color folder contains the color images and corresponding labels.
Depth folder contains the height information of scene from the floor in mm unit and saved as uint16 format.
RGBD folder contains the combined pairs of color and depth images. The normalized height information is added as 4th channel (RGBA).
Each folder contains a labels folder for the corresponding labeling information

🧠 Use Cases

Animal behavior monitoring
Multimodal object detection (RGB + Depth)
Precision livestock farming

License

The author has granted permission to download, use and redistribute this dataset only for research purposes.

Citation

Please cite as Bhujel A. et al. (2025). A Computer Vision dataset for Gilts' daily activity monitoring and tracking.

Contact

For questions or collaborations, feel free to reach out at [email protected]